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008 150903s2009 xxu| o |||| 0|eng d
020 _a9780387858302
_99780387858302
024 7 _a10.1007/9780387858302
_2doi
035 _avtls000333145
039 9 _a201509030214
_bVLOAD
_c201404122352
_dVLOAD
_c201404092132
_dVLOAD
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_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
100 1 _aMinker, Wolfgang.
_eautor
_9303483
245 1 0 _aIncorporating Knowledge Sources into Statistical Speech Recognition /
_cby Wolfgang Minker, Satoshi Nakamura, Konstantin Markov, Sakriani Sakti.
264 1 _aBoston, MA :
_bSpringer US,
_c2009.
300 _brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aLecture Notes in Electrical Engineering,
_x1876-1100 ;
_v42
500 _aSpringer eBooks
505 0 _aand Book Overview -- Statistical Speech Recognition -- Graphical Framework to Incorporate Knowledge Sources -- Speech Recognition Using GFIKS -- Conclusions and Future Directions.
520 _aIncorporating Knowledge Sources into Statistical Speech Recognition offers solutions for enhancing the robustness of a statistical automatic speech recognition (ASR) system by incorporating various additional knowledge sources while keeping the training and recognition effort feasible. The authors provide an efficient general framework for incorporating knowledge sources into state-of-the-art statistical ASR systems. This framework, which is called GFIKS (graphical framework to incorporate additional knowledge sources), was designed by utilizing the concept of the Bayesian network (BN) framework. This framework allows probabilistic relationships among different information sources to be learned, various kinds of knowledge sources to be incorporated, and a probabilistic function of the model to be formulated. Incorporating Knowledge Sources into Statistical Speech Recognition demonstrates how the statistical speech recognition system may incorporate additional information sources by utilizing GFIKS at different levels of ASR. The incorporation of various knowledge sources, including background noises, accent, gender and wide phonetic knowledge information, in modeling is discussed theoretically and analyzed experimentally.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aNakamura, Satoshi.
_eautor
_9305637
700 1 _aMarkov, Konstantin.
_eautor
_9305638
700 1 _aSakti, Sakriani.
_eautor
_9305639
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9780387858296
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-85830-2
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999 _c280598
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